000			01767 a2200265 4500
999			_c2641 _d2641
005			20241015152519.0
008			241015b ||||| |||| 00| 0 eng d
020			_a9781611975598
041			_aeng
082			_a519.6 GIL/P
100			_aGill, Philip E _99926
100			_aMurray, Walter _99927
100			_aWright, Margaret H _99928
245			_aPractical optimization
260			_bSIAM -- _c2019 _aUnited States of America --
300			_axvi, 401p.
520			_aIn the intervening years since this book was published in 1981, the field of optimization has been exceptionally lively. This fertility has involved not only progress in theory, but also faster numerical algorithms and extensions into unexpected or previously unknown areas such as semidefinite programming. Despite these changes, many of the important principles and much of the intuition can be found in this Classics version of Practical Optimization. This book provides model algorithms and pseudocode, useful tools for users who prefer to write their own code as well as for those who want to understand externally provided code; presents algorithms in a step-by-step format, revealing the overall structure of the underlying procedures and thereby allowing a high-level perspective on the fundamental differences; and contains a wealth of techniques and strategies that are well suited for optimization in the twenty-first century and particularly in the now-flourishing fields of data science, "big data," and machine learning.
650			_aMathematics _99929
650			_aProbability _92337
650			_aComputational optimization _99930
650			_aMathematical programming _99931
650			_aNumerical analysis _9121
650			_aOptimization _99932
942			_cBK